Density, distribution function, quantile function and random generation for the multilook intensity Gamma law parametrised by its mean \(\mu\) and number of looks \(L\): $$f(z) = \frac{L^L}{\mu^L \Gamma(L)} z^{L-1} \exp(-L z / \mu), \quad z > 0.$$ This is the model for homogeneous areas (fully developed speckle).
Usage
dgammasar(x, mu, L, log = FALSE)
pgammasar(q, mu, L, lower.tail = TRUE, log.p = FALSE)
qgammasar(p, mu, L, lower.tail = TRUE, log.p = FALSE)
rgammasar(n, mu, L)Arguments
- x, q
Vector of quantiles.
- mu
Mean (\(\mu > 0\)).
- L
Number of looks (\(L > 0\)).
- log, log.p
Logical; if
TRUE, probabilities/densities are given as logarithms.- lower.tail
Logical; if
TRUE(default) probabilities are \(P(X \le x)\), otherwise \(P(X > x)\).- p
Vector of probabilities.
- n
Number of observations.
See also
GammaSAR for the R6 interface.
Examples
dgammasar(c(0.5, 1, 2), mu = 1, L = 4)
#> [1] 0.7217882 0.7814673 0.1145046
rgammasar(3, mu = 1, L = 4)
#> [1] 0.3494577 1.4005644 0.6856437